Machine Learning Analysis Pipeline

EDR: Dataset Loading & Preprocessing

EDR – Train/Test Overview
• Train shape: (17536, 20) | Test shape: (1535, 20)
• Total train samples: 17,536 | Total test samples: 1,535
• Number of features: 18
• Target column: 'label'
• Missing values (train): 0 | (test): 0

EDR – Train Class Distribution
• 0: 16,679
• 1: 857
• Class balance (minority/majority): 5.1382%

EDR – Feature Preparation
• Target encoding: {0: 0, 1: 1}
• Data preprocessing: Infinite values handled, missing values filled with train medians
• Feature scaling: StandardScaler (fit on train, applied to test)

Baseline (Most-Frequent) Accuracy: 0.9518

EDR: Model Performance Comparison

EDR – Model Performance Metrics

Model	Accuracy	Balanced Acc	Precision	Recall	F1	ROC-AUC	PR-AUC
Logistic Regression	0.9303	0.5849	0.2381	0.2027	0.2190	0.6400	0.1425
Random Forest (SMOTE)	0.9414	0.5715	0.3000	0.1622	0.2105	0.7280	0.1860
LightGBM	0.9440	0.5536	0.3000	0.1216	0.1731	0.8489	0.2193
Balanced RF	0.8925	0.6677	0.2026	0.4189	0.2731	0.8290	0.1893
SGD SVM	0.0860	0.5198	0.0501	1.0000	0.0954	nan	nan
IsolationForest	0.8397	0.5502	0.0825	0.2297	0.1214	nan	nan

Confusion Matrix Analysis

Model	TN	FP	FN	TP	FP Rate	Miss Rate
Logistic Regression	1413	48	59	15	3.29%	79.73%
Random Forest (SMOTE)	1433	28	62	12	1.92%	83.78%
LightGBM	1440	21	65	9	1.44%	87.84%
Balanced RF	1339	122	43	31	8.35%	58.11%
SGD SVM	58	1403	0	74	96.03%	0.00%
IsolationForest	1272	189	57	17	12.94%	77.03%

Best Models by Metric

Accuracy

LightGBM

0.9440

Balanced Acc

Balanced RF

0.6677

Precision

Random Forest (SMOTE)

0.3000

Recall

SGD SVM

1.0000

Balanced RF

0.2731

ROC-AUC

LightGBM

0.8489

PR-AUC

LightGBM

0.2193

Lowest False Positive Rate

LightGBM

1.44%

Lowest Miss Rate

SGD SVM

0.00%

EDR – Metrics by Model

EDR – ROC Curves

EDR – Precision–Recall Curves

EDR – Predicted Probability Distributions

EDR – Threshold Sweep

EDR: Logistic Regression – Detailed Analysis

EDR – Logistic Regression: Confusion Matrix

EDR – Logistic Regression: Classification Report

Model	precision	recall	f1	support
0	0.9599	0.9671	0.9635	1461.0000
1	0.2381	0.2027	0.2190	74.0000
accuracy	nan	nan	0.9303	1535.0000

EDR – Logistic Regression: Feature Importance

EDR: Random Forest (SMOTE) – Detailed Analysis

EDR – Random Forest (SMOTE): Confusion Matrix

EDR – Random Forest (SMOTE): Classification Report

Model	precision	recall	f1	support
0	0.9585	0.9808	0.9696	1461.0000
1	0.3000	0.1622	0.2105	74.0000
accuracy	nan	nan	0.9414	1535.0000

EDR – Random Forest (SMOTE): Feature Importance

EDR: LightGBM – Detailed Analysis

EDR – LightGBM: Confusion Matrix

EDR – LightGBM: Classification Report

Model	precision	recall	f1	support
0	0.9568	0.9856	0.9710	1461.0000
1	0.3000	0.1216	0.1731	74.0000
accuracy	nan	nan	0.9440	1535.0000

EDR – LightGBM: Feature Importance

EDR: Balanced RF – Detailed Analysis

EDR – Balanced RF: Confusion Matrix

EDR – Balanced RF: Classification Report

Model	precision	recall	f1	support
0	0.9689	0.9165	0.9420	1461.0000
1	0.2026	0.4189	0.2731	74.0000
accuracy	nan	nan	0.8925	1535.0000

EDR – Balanced RF: Feature Importance

EDR: SGD SVM – Detailed Analysis

EDR – SGD SVM: Confusion Matrix

EDR – SGD SVM: Classification Report

Model	precision	recall	f1	support
0	1.0000	0.0397	0.0764	1461.0000
1	0.0501	1.0000	0.0954	74.0000
accuracy	nan	nan	0.0860	1535.0000

EDR – SGD SVM: Feature Importance

EDR: IsolationForest – Detailed Analysis

EDR – IsolationForest: Confusion Matrix

EDR – IsolationForest: Classification Report

Model	precision	recall	f1	support
0	0.9571	0.8706	0.9118	1461.0000
1	0.0825	0.2297	0.1214	74.0000
accuracy	nan	nan	0.8397	1535.0000

EDR – IsolationForest: Feature Importance

Feature importance not available for this model type.

XDR: Dataset Loading & Preprocessing

XDR – Train/Test Overview
• Train shape: (17536, 34) | Test shape: (1535, 34)
• Total train samples: 17,536 | Total test samples: 1,535
• Number of features: 32
• Target column: 'label'
• Missing values (train): 0 | (test): 0

XDR – Train Class Distribution
• 0: 16,679
• 1: 857
• Class balance (minority/majority): 5.1382%

XDR – Feature Preparation
• Target encoding: {0: 0, 1: 1}
• Data preprocessing: Infinite values handled, missing values filled with train medians
• Feature scaling: StandardScaler (fit on train, applied to test)

Baseline (Most-Frequent) Accuracy: 0.9518

XDR: Model Performance Comparison

XDR – Model Performance Metrics

Model	Accuracy	Balanced Acc	Precision	Recall	F1	ROC-AUC	PR-AUC
Logistic Regression	0.7420	0.6271	0.0934	0.5000	0.1574	0.6204	0.1363
Random Forest (SMOTE)	0.9414	0.5587	0.2778	0.1351	0.1818	0.6934	0.1801
LightGBM	0.9492	0.5628	0.4167	0.1351	0.2041	0.8370	0.2122
Balanced RF	0.9036	0.6928	0.2394	0.4595	0.3148	0.8368	0.1678
SGD SVM	0.1173	0.5299	0.0512	0.9865	0.0973	nan	nan
IsolationForest	0.9199	0.5474	0.1449	0.1351	0.1399	nan	nan

Confusion Matrix Analysis

Model	TN	FP	FN	TP	FP Rate	Miss Rate
Logistic Regression	1102	359	37	37	24.57%	50.00%
Random Forest (SMOTE)	1435	26	64	10	1.78%	86.49%
LightGBM	1447	14	64	10	0.96%	86.49%
Balanced RF	1353	108	40	34	7.39%	54.05%
SGD SVM	107	1354	1	73	92.68%	1.35%
IsolationForest	1402	59	64	10	4.04%	86.49%

Best Models by Metric

Accuracy

LightGBM

0.9492

Balanced Acc

Balanced RF

0.6928

Precision

LightGBM

0.4167

Recall

SGD SVM

0.9865

Balanced RF

0.3148

ROC-AUC

LightGBM

0.8370

PR-AUC

LightGBM

0.2122

Lowest False Positive Rate

LightGBM

0.96%

Lowest Miss Rate

SGD SVM

1.35%

XDR – Metrics by Model

XDR – ROC Curves

XDR – Precision–Recall Curves

XDR – Predicted Probability Distributions

XDR – Threshold Sweep

XDR: Logistic Regression – Detailed Analysis

XDR – Logistic Regression: Confusion Matrix

XDR – Logistic Regression: Classification Report

Model	precision	recall	f1	support
0	0.9675	0.7543	0.8477	1461.0000
1	0.0934	0.5000	0.1574	74.0000
accuracy	nan	nan	0.7420	1535.0000

XDR – Logistic Regression: Feature Importance

XDR: Random Forest (SMOTE) – Detailed Analysis

XDR – Random Forest (SMOTE): Confusion Matrix

XDR – Random Forest (SMOTE): Classification Report

Model	precision	recall	f1	support
0	0.9573	0.9822	0.9696	1461.0000
1	0.2778	0.1351	0.1818	74.0000
accuracy	nan	nan	0.9414	1535.0000

XDR – Random Forest (SMOTE): Feature Importance

XDR: LightGBM – Detailed Analysis

XDR – LightGBM: Confusion Matrix

XDR – LightGBM: Classification Report

Model	precision	recall	f1	support
0	0.9576	0.9904	0.9738	1461.0000
1	0.4167	0.1351	0.2041	74.0000
accuracy	nan	nan	0.9492	1535.0000

XDR – LightGBM: Feature Importance

XDR: Balanced RF – Detailed Analysis

XDR – Balanced RF: Confusion Matrix

XDR – Balanced RF: Classification Report

Model	precision	recall	f1	support
0	0.9713	0.9261	0.9481	1461.0000
1	0.2394	0.4595	0.3148	74.0000
accuracy	nan	nan	0.9036	1535.0000

XDR – Balanced RF: Feature Importance

XDR: SGD SVM – Detailed Analysis

XDR – SGD SVM: Confusion Matrix

XDR – SGD SVM: Classification Report

Model	precision	recall	f1	support
0	0.9907	0.0732	0.1364	1461.0000
1	0.0512	0.9865	0.0973	74.0000
accuracy	nan	nan	0.1173	1535.0000

XDR – SGD SVM: Feature Importance

XDR: IsolationForest – Detailed Analysis

XDR – IsolationForest: Confusion Matrix

XDR – IsolationForest: Classification Report

Model	precision	recall	f1	support
0	0.9563	0.9596	0.9580	1461.0000
1	0.1449	0.1351	0.1399	74.0000
accuracy	nan	nan	0.9199	1535.0000

XDR – IsolationForest: Feature Importance

Feature importance not available for this model type.

Machine Learning Analysis Report

Machine Learning Analysis Pipeline

EDR: Dataset Loading & Preprocessing

EDR: Model Performance Comparison

EDR – Model Performance Metrics

Confusion Matrix Analysis

Best Models by Metric

EDR – Metrics by Model

EDR – ROC Curves

EDR – Precision–Recall Curves

EDR – Predicted Probability Distributions

EDR – Threshold Sweep

EDR: Logistic Regression – Detailed Analysis

EDR – Logistic Regression: Confusion Matrix

EDR – Logistic Regression: Confusion Matrix

EDR – Logistic Regression: Classification Report

EDR – Logistic Regression: Feature Importance

EDR – Logistic Regression: Feature Importance

EDR: Random Forest (SMOTE) – Detailed Analysis

EDR – Random Forest (SMOTE): Confusion Matrix

EDR – Random Forest (SMOTE): Confusion Matrix

EDR – Random Forest (SMOTE): Classification Report

EDR – Random Forest (SMOTE): Feature Importance

EDR – Random Forest (SMOTE): Feature Importance

EDR: LightGBM – Detailed Analysis

EDR – LightGBM: Confusion Matrix

EDR – LightGBM: Confusion Matrix

EDR – LightGBM: Classification Report

EDR – LightGBM: Feature Importance

EDR – LightGBM: Feature Importance

EDR: Balanced RF – Detailed Analysis

EDR – Balanced RF: Confusion Matrix

EDR – Balanced RF: Confusion Matrix

EDR – Balanced RF: Classification Report

EDR – Balanced RF: Feature Importance

EDR – Balanced RF: Feature Importance

EDR: SGD SVM – Detailed Analysis

EDR – SGD SVM: Confusion Matrix

EDR – SGD SVM: Confusion Matrix

EDR – SGD SVM: Classification Report

EDR – SGD SVM: Feature Importance

EDR – SGD SVM: Feature Importance

EDR: IsolationForest – Detailed Analysis

EDR – IsolationForest: Confusion Matrix

EDR – IsolationForest: Confusion Matrix

EDR – IsolationForest: Classification Report

EDR – IsolationForest: Feature Importance

XDR: Dataset Loading & Preprocessing

XDR: Model Performance Comparison

XDR – Model Performance Metrics

Confusion Matrix Analysis

Best Models by Metric

XDR – Metrics by Model

XDR – ROC Curves

XDR – Precision–Recall Curves

XDR – Predicted Probability Distributions

XDR – Threshold Sweep

XDR: Logistic Regression – Detailed Analysis

XDR – Logistic Regression: Confusion Matrix

XDR – Logistic Regression: Confusion Matrix

XDR – Logistic Regression: Classification Report

XDR – Logistic Regression: Feature Importance

XDR – Logistic Regression: Feature Importance

XDR: Random Forest (SMOTE) – Detailed Analysis

XDR – Random Forest (SMOTE): Confusion Matrix

XDR – Random Forest (SMOTE): Confusion Matrix

XDR – Random Forest (SMOTE): Classification Report

XDR – Random Forest (SMOTE): Feature Importance

XDR – Random Forest (SMOTE): Feature Importance

XDR: LightGBM – Detailed Analysis

XDR – LightGBM: Confusion Matrix

XDR – LightGBM: Confusion Matrix

XDR – LightGBM: Classification Report

XDR – LightGBM: Feature Importance

XDR – LightGBM: Feature Importance

XDR: Balanced RF – Detailed Analysis

XDR – Balanced RF: Confusion Matrix

XDR – Balanced RF: Confusion Matrix

XDR – Balanced RF: Classification Report

XDR – Balanced RF: Feature Importance